Response to Information Privacy

What does the EU's "right to be forgotten" ruling require and not require?

The decision handed down by the ECJ in a dispute between a Spanish man, who was suing a Spanish newspaper (to be precise, its website), and Google Spain has sent shockwaves through the internet industry. In effect, the judges have reiterated that in Europe there is a "right to be forgotten" - which applies specifically to search engines but not to news websites (or other journalistic endeavour) and that people can therefore ask to be removed from search indexes.

What arguments did the NPR panelists make in favor and against the idea of adopting similar legislation in the USA?

@Arguments against the idea

 There main point was brought by stating that the "right to be forgotten" is censorship. That ability, that right to remember is one of the most fundamental rights of being a human being. By suppressing true information, not libel, not defamation, not hate speech. True information intrudes into our collective memory by trying to suppress thedocuments that constitute that memory. So, it talks about the regulation ofspeech and, really, the regulation of thought which is ultimately what memory is. So,the law is more like the right to force people to forget true information.The problem, though, is that people have the right to control what they remember. theyhave the right to control what they say. they do not have the right to control what othersremember. People do not have the right to control what others say. And so for that reason, wemust treat the "right to be forgotten" as it is, which is a form of censorship. So, as said,censorship in a free and democratic society needs to clear a very high bar to bejustified. The "right to be forgotten" does not meet that bar.         
  
      "First of all, it's way too prone to abuse. It is vague, it is subjective.”


The better way to do it is that wecould have some kind of court mechanism adjudicate the "right to be forgotten" andcome up with a body of case law that would define the bounds of the right, flush it outand so forth.

@Arguments in favor

 One can control data about him and his profile. When you putin the name of an individual, Google gives you more than anything else, any othersource of information about this person, and the law made in EU requires that in the same way that people can ask data brokers, the bank, they can ask in school after acertain time for things to be deleted, they should be able to ask Google fordeletion. Now, the person in question who brought this case from Spain, he had notpaid some social contributions and therefore his house had been confiscated and it wasa legal obligation in Spain to have a publication in a newspaper of this fact in order tomake the auctioning of the house to cover the social contributions better and attractivefor the Spanish state, because if it's in the newspaper there will be more people biddingfor this house.So, the court said you cannot ask the newspaper to take down the information becausethey have a legal obligation to publish, but Google you can ask. And this is a principlewhich also applies when it comes to free speech and democracy. It may well be thatGoogle has to put things -- take things down, but it doesn't mean the informationdisappears. It will stay in the newspaper. It stays on the website of the newspaper ofthe BBC, of the television station, all this stays around, but Google is subject to the samelaw of self-control of information, of self-determination of individuals, which allowsthem to ask first what do you have about me and second, please delete.


 

Where do you stand? Should we have similar legislation in the US? What would be the benefits and what would be the drawbacks?

In a free country, you as an individual must have the right to control what others know aboutyou. You must be able to ask “What do you have?” and you must be able to ask fordeletion, of course within reason. You cannot ask a doctor to delete the medicalrecords, because the doctor must keep them either to show what he has done or shehas done in terms of liability. Of course you can't ask the press to delete a bad review ifyou are a concert pianist, or for that matter if you are someone who has donemalpractice or if you are a politician. There are limits to this, which are limits which areimportant in a democratic society, because that's what you will hear from the otherside, that this is an attack on democracy.I believe democracy needs both. We needs privacy. We need the ability of theindividual to decide himself or herself what the state in particular knows about them,because if you don't have the privacy, how can you organize dissent? How can youorganize a new political party, which maybe wants to, if already thegovernment always knows everything about you. So, we need privacy in a free society as we need free speech.

Response to Data Science

• The application of machine learning to hiring and promoting employees promises to alleviate some of the biases created by current techniques that rely more on human intuition. What biases in the HR hiring and promotion process are the people analytics systems trying to eliminate, and how do they do it. What new biases might these algorithms introduce?


There are many significant biases in the HR hiring and promotion process that the people analytics systems trying to eliminate. One of them being appearance,Tall men get hired and promoted more frequently than short men, and make more money. Beautiful women get preferential treatment, too. one of the best example being survey done by Duke’s Fuqua School of Business where they found that there was no relationship  between how competent a CEO looked and the financial performance of his or her company.


"Now days hiring managers just don’t even want to interview anymore they just want to hire the people with the highest scores."


Companies want great Aptitude, skills, personal history, psychological stability, discretion and loyalty which can not be tested with just looking at one’s resume but machine learning helps them to go through this and choose the best.And algorithms helps them to do so perhaps the most exotic development in people analytics today is the creation of algorithms to assess the potential of all workers, across all companies, all the time.


Hiring managers  assess the way coders use language on social networks from LinkedIn to Twitter; companies have determined that certain phrases and words used in association with one another can distinguish expert programmers from less skilled ones. Over time, better job-matching technologies are likely to begin serving people directly, helping them see more clearly which jobs might suit them and which companies could use their skills, this will surely allow hiring companies as well as people finding jobs to connect and save time.


The application of machine learning to hiring and promoting employees is certainly alleviating some of the biases created by current techniques that rely more on human intuition.


“Consider knack”, a tiny start-up based in Silicon Valley came up with a great idea to hire people, they created games. These games aren’t just for play: they’ve been designed by a team of neuroscientists, psychologists, and data scientists to suss out human potential.
Without ever seeing the ideas, without meeting or interviewing the people who’d proposed them, without knowing their title or background or academic pedigree, algorithm had identified the people whose ideas had panned out.
It offers a way for the hiring managers to avoid wasting time on the 80 people out of 100—nearly all of whom look smart, well-trained, and plausible on paper—whose ideas just aren’t likely to work out and then they can devote much more careful attention to the 20 people out of 100 whose ideas have the most merit.
In the late 1990s, as these assessments shifted from paper to digital formats and proliferated, data scientists started doing massive tests of what makes for a successful customer-support technician or salesperson. This has unquestionably improved the quality of the workers at many firms.


Company called Xerox switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away). Those candidates who score best, I learned, tend to exhibit a creative but not overly inquisitive personality, and participate in at least one but not more than four social networks, among many other factors.When Xerox started using the score in its hiring decisions, the quality of its hires immediately improved. The rate of attrition fell by 20 percent in the initial pilot period, and over time, the number of promotions rose


The potential power of this data-rich approach is obvious. What begins with an online screening test for entry-level workers ends with the transformation of nearly every aspect of hiring, performance assessment, and management. In theory, this approach enables companies to fast-track workers for promotion based on their statistical profiles; to assess managers more scientifically; even to match workers and supervisors who are likely to perform well together, based on the mix of their competencies and personalities.


But there is even a different side to it, there are some new biases that these algorithms might introduce We are leaving the evaluation on specific algorithms that are already set and choose specifically. Don’t you think ‘data signature’ of natural leaders play a role in promotion. These are all live questions today, and they prompt heavy concerns: that we will cede one of the most subtle and human of skills, the evaluation of the gifts and promise of other people, to machines; that the models will get it wrong; that some people will never get a shot in the new workforce.




• Considering that most of you will be interviewing for jobs soon, does this application of machine learning to human resources seem fair to you? What's your reaction?




It is pretty true that over the past couple of generations, colleges and universities have become the gatekeepers to a prosperous life. A degree has become a signal of intelligence and conscientiousness, one that grows stronger the more selective the school and the higher a student’s GPA, that is easily understood by employers, and that, until the advent of people analytics, was probably unrivaled in its predictive powers. But this relationship is likely to loosen in the coming years and hiring using algorithms will take over future.  The use of machine learning to choose human resource is a definitely a good idea but, I think that sometimes it is also important for the hirer to understand one more than his brain, like one’s body language can tell a lot of things about one. So a mixture of both algorithms and human intuition will work best.  

Response to Information Architecture

• According to Morville and Rosenfeld, what are the basic schemes one can use to organize information? As you describe each scheme, also describe what kind of information the scheme is best suited for.

What is an organization scheme? 

“Organization scheme defines the shared characteristics of content items and influences the logical grouping of those items.”

We navigate through organization schemes every day. Telephone books, supermarkets, and television programming guides all use organization schemes to facilitate access.

               1.    Exact Organization Schemes

The scheme is best suited for the users who know the specific name of the resource they are looking for. The schemes won’t  work very well if user is looking for a general type, for example electrician, plumber, etc. It will show you what you have saved with specific name and words.

1. Alphabetical

As the name suggests an alphabetical organization scheme uses alphabetical order to find something. This scheme is the primary organization scheme for encyclopedias and dictionaries.

2. Chronological 

Sometimes information has to be saved with reference to date and the event that had happened on a specific date. Usually History books, magazine archives, diaries, and television guides tend to be organized chronologically.

3. Geographical

Schemes that uses location to organize data like weather, shops located, and the most latest news around the world is organized Geographically. With the exception of border disputes, geographical organization schemes are fairly straightforward to design and use. 

                2.   Ambiguous Organization Schemes

Again, as the name suggests Ambiguous or “subjective” organization schemes divide information into categories that defy exact definition. Although they are difficult to design, they are mired in the ambiguity of language and organization, not to mention human subjectivity. Here the user can search the whole group without being specific. 

1. Topic

This scheme is the most common and useful scheme as it organizes information by subject or topic which makes it challenging approaches to develop. Academic courses and departments, newspapers, and the chapters of most nonfiction books are all organized along topical lines.

2. Task

Task-oriented schemes organize content and applications into a collection of processes, functions, or tasks. These schemes are appropriate when it’s possible to anticipate a limited number of high-priority tasks that users will want to perform.

3. Audience

In cases where there are two or more clearly definable audiences for a web site or intranet, an audience-specific organization scheme may make sense. This type of scheme works best when the site is frequented by repeat visitors who can bookmark their particular section of the site. It also works well if there is value in customizing the content for each audience.

4. Metaphor

It means creating similar options for similar tasks. Metaphors are commonly used to help users understand the new by relating it to the familiar. for example using trash can as the icon to delete files on computer.

5. Hybrids

The perfect combinations of all the schemes bring us to Hybrid scheme. The power of a pure organization scheme derives from its ability to suggest a simple mental model that users can quickly understand. One of the best example can be google search bar.

• Why did Netflix develop their microgenre feature, and how did they categorize their movies? Why didn't they just let end-users tag movies like Flickr or YouTube allow?

Netflix possesses 76,897 unique ways to describe types of movies.

Netflix makes it wonderful program work by using large teams of people specially trained to watch movies. They are paid people to watch films and tag them with all kinds of metadata. This process is so sophisticated and precise that taggers receive a 36-page training document that teaches them how to rate movies on their sexually suggestive content, goriness, romance levels, and even narrative elements like plot conclusiveness.

They capture dozens of different movie attributes. They even rate the moral status of characters. When these tags are combined with millions of users viewing habits, they become Netflix's competitive advantage. These tags are then run down in a algorithm which helps Netflix to distribute movies in specific categories.  The company's main goal as a business is to gain and retain subscribers. And the genres that it displays to people are a key part of that strategy as it makes the interface user friendly and allows user to quickly select the best movie they could watch at that point of time, which definitely increases the numbers of views and users.

And now, they have a terrific advantage in their efforts to produce their own content: Netflix has created a database of American cinematic predilections.

“Yellin said that the genres were limited by three main factors: 1) they only want to display 50 characters for various UI reasons, which eliminates most long genres; 2) there had to be a "critical mass" of content that fit the description of the genre, at least in Netflix's extended DVD catalog; and 3) they only wanted genres that made syntactic sense.” This kind of categorization is actually very good and is based on hybrid organizational scheme.

They didn’t just let end-users tag movies like Flickr or YouTube allow because 

Netflix had decided to "go beyond the 5 stars," which is where the personalized genres come in and made this a whole new experience.